The field of life sciences has experienced dramatic advancement over the last two decades. From the broad commercialization of products that derive from recombinant deoxyribonucleic acid (DNA) technology, to the simplification of research, development and diagnostics, enabled by critical research tools, such as the polymerase chain reaction, nucleic acid array technologies, robust nucleic acid sequencing technologies, and more recently, the development and commercialization of high throughput next generation sequencing technologies. All of these improvements have combined to advance the fields of biological research, medicine, diagnostics, agricultural biotechnology, and myriad other related fields by leaps and bounds.
None of these technologies generally exist in a vacuum, but instead are integrated into a broader workflow that includes upstream components of sample gathering and preparation, to the downstream components of data gathering, deconvolution, interpretation and ultimately exploitation. Further, each of these advancements, while marking a big step forward for their fields, has tended to expose critical bottlenecks in the workflows that must, themselves, evolve to fit the demands of the field. For example, genome sequencing is bounded on both ends by critical workflow issues, including, in many cases, complex and labor intensive sample preparation processes, just to be able to begin sequencing nucleic acids from sample materials. Likewise, once sequence data is obtained, there is a complex back-end informatics requirement in order to deconvolve the sequence data into base calls, and then assemble the determined base sequences into contiguous sequence data, and ultimately align that sequence data to whole genomes for a given organism.
One critical bottleneck for many of these technologies lies not in their ability to generate massive amounts of data, but in the ability to more specifically attribute that data to a portion of a complex sample, or to a given sample among many multiplexed samples.